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<Paper uid="N04-1029">
  <Title>Comparison of Two Interactive Search Refinement Techniques</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Query expansion following relevance feedback is a well established technique in information retrieval, which aims at improving user search performance. It combines user and system effort towards selecting and adding extra terms to the original query. The traditional model of query expansion following relevance feedback is as follows: the user reads a representation of a retrieved document, typically its full-text or abstract, and provides the system with a binary relevance judgement.</Paragraph>
    <Paragraph position="1"> After that the system extracts query expansion terms from the document, which are then added to the query either manually by the searcher - interactive query expansion, or automatically - automatic query expansion. Intuitively interactive query expansion should produce better results than automatic, however this is not consistently so (Beaulieu 1997, Koenemann and Belkin 1996, Ruthven 2003).</Paragraph>
    <Paragraph position="2"> In this paper we present two new approaches to automatic and interactive query expansion, which we developed and tested within the framework of the High</Paragraph>
  </Section>
class="xml-element"></Paper>